# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os, sys, time, random, csv, datetime, json import pandas as pd import numpy as np import argparse import logging import time logging.basicConfig( format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger("preprocess") logger.setLevel(logging.INFO) TRAIN_QUERIES_PATH = "./data_set_phase1/train_queries.csv" TRAIN_PLANS_PATH = "./data_set_phase1/train_plans.csv" TRAIN_CLICK_PATH = "./data_set_phase1/train_clicks.csv" PROFILES_PATH = "./data_set_phase1/profiles.csv" OUT_NORM_TRAIN_PATH = "./out/normed_train.txt" OUT_RAW_TRAIN_PATH = "./out/train.txt" OUT_DIR = "./out" O1_MIN = 115.47 O1_MAX = 117.29 O2_MIN = 39.46 O2_MAX = 40.97 D1_MIN = 115.44 D1_MAX = 117.37 D2_MIN = 39.46 D2_MAX = 40.96 SCALE_OD = 0.02 DISTANCE_MIN = 1.0 DISTANCE_MAX = 225864.0 THRESHOLD_DIS = 40000.0 SCALE_DIS = 500 PRICE_MIN = 200.0 PRICE_MAX = 92300.0 THRESHOLD_PRICE = 20000 SCALE_PRICE = 100 ETA_MIN = 1.0 ETA_MAX = 72992.0 THRESHOLD_ETA = 10800.0 SCALE_ETA = 120 def build_norm_feature(): with open(OUT_NORM_TRAIN_PATH, 'w') as nf: with open(OUT_RAW_TRAIN_PATH, 'r') as f: for line in f: cur_map = json.loads(line) if cur_map["plan"]["distance"] > THRESHOLD_DIS: cur_map["plan"]["distance"] = int(THRESHOLD_DIS) elif cur_map["plan"]["distance"] > 0: cur_map["plan"]["distance"] = int(cur_map["plan"]["distance"] / SCALE_DIS) if cur_map["plan"]["price"] and cur_map["plan"]["price"] > THRESHOLD_PRICE: cur_map["plan"]["price"] = int(THRESHOLD_PRICE) elif not cur_map["plan"]["price"] or cur_map["plan"]["price"] < 0: cur_map["plan"]["price"] = 0 else: cur_map["plan"]["price"] = int(cur_map["plan"]["price"] / SCALE_PRICE) if cur_map["plan"]["eta"] > THRESHOLD_ETA: cur_map["plan"]["eta"] = int(THRESHOLD_ETA) elif cur_map["plan"]["eta"] > 0: cur_map["plan"]["eta"] = int(cur_map["plan"]["eta"] / SCALE_ETA) # o1 if cur_map["query"]["o1"] > O1_MAX: cur_map["query"]["o1"] = int((O1_MAX - O1_MIN) / SCALE_OD + 1) elif cur_map["query"]["o1"] < O1_MIN: cur_map["query"]["o1"] = 0 else: cur_map["query"]["o1"] = int((cur_map["query"]["o1"] - O1_MIN) / 0.02) # o2 if cur_map["query"]["o2"] > O2_MAX: cur_map["query"]["o2"] = int((O2_MAX - O2_MIN) / SCALE_OD + 1) elif cur_map["query"]["o2"] < O2_MIN: cur_map["query"]["o2"] = 0 else: cur_map["query"]["o2"] = int((cur_map["query"]["o2"] - O2_MIN) / 0.02) # d1 if cur_map["query"]["d1"] > D1_MAX: cur_map["query"]["d1"] = int((D1_MAX - D1_MIN) / SCALE_OD + 1) elif cur_map["query"]["d1"] < D1_MIN: cur_map["query"]["d1"] = 0 else: cur_map["query"]["d1"] = int((cur_map["query"]["d1"] - D1_MIN) / SCALE_OD) # d2 if cur_map["query"]["d2"] > D2_MAX: cur_map["query"]["d2"] = int((D2_MAX - D2_MIN) / SCALE_OD + 1) elif cur_map["query"]["d2"] < D2_MIN: cur_map["query"]["d2"] = 0 else: cur_map["query"]["d2"] = int((cur_map["query"]["d2"] - D2_MIN) / SCALE_OD) cur_json_instance = json.dumps(cur_map) nf.write(cur_json_instance + '\n') def preprocess(): """ Construct the train data indexed by session id and mode id jointly. Convert all the raw features (user profile, od pair, req time, click time, eta, price, distance, transport mode) to one-hot ids used for embedding. We split the one-hot features into two categories: user feature and context feature for better understanding of FM algorithm. Note that the user profile is already provided by one-hot encoded form, we treat it as embedded vector for unity with the context feature and easily using of PaddlePaddle embedding layer. Given the train clicks data, we label each train instance with 1 or 0 depend on if this instance is clicked or not include non-click case. :return: """ train_data_dict = {} with open(TRAIN_QUERIES_PATH, 'r') as f: csv_reader = csv.reader(f, delimiter=',') train_index_list = [] for k, line in enumerate(csv_reader): if k == 0: continue if line[0] == "": continue if line[1] == "": train_index_list.append(line[0] + "_0") else: train_index_list.append(line[0] + "_" + line[1]) train_index = line[0] train_data_dict[train_index] = {} train_data_dict[train_index]["pid"] = line[1] train_data_dict[train_index]["query"] = {} reqweekday = datetime.datetime.strptime(line[2], '%Y-%m-%d %H:%M:%S').strftime("%w") reqhour = datetime.datetime.strptime(line[2], '%Y-%m-%d %H:%M:%S').strftime("%H") train_data_dict[train_index]["query"].update({"weekday":reqweekday}) train_data_dict[train_index]["query"].update({"hour":reqhour}) o = line[3].split(',') o_first = o[0] o_second = o[1] train_data_dict[train_index]["query"].update({"o1":float(o_first)}) train_data_dict[train_index]["query"].update({"o2":float(o_second)}) d = line[4].split(',') d_first = d[0] d_second = d[1] train_data_dict[train_index]["query"].update({"d1":float(d_first)}) train_data_dict[train_index]["query"].update({"d2":float(d_second)}) plan_map = {} plan_data = pd.read_csv(TRAIN_PLANS_PATH) for index, row in plan_data.iterrows(): plans_str = row['plans'] plans_list = json.loads(plans_str) session_id = str(row['sid']) # train_data_dict[session_id]["plans"] = [] plan_map[session_id] = plans_list profile_map = {} with open(PROFILES_PATH, 'r') as f: csv_reader = csv.reader(f, delimiter=',') for k, line in enumerate(csv_reader): if k == 0: continue profile_map[line[0]] = [i for i in range(len(line)) if line[i] == "1.0"] session_click_map = {} with open(TRAIN_CLICK_PATH, 'r') as f: csv_reader = csv.reader(f, delimiter=',') for k, line in enumerate(csv_reader): if k == 0: continue if line[0] == "" or line[1] == "" or line[2] == "": continue session_click_map[line[0]] = line[2] #return train_data_dict, profile_map, session_click_map, plan_map generate_sparse_features(train_data_dict, profile_map, session_click_map, plan_map) def generate_sparse_features(train_data_dict, profile_map, session_click_map, plan_map): if not os.path.isdir(OUT_DIR): os.mkdir(OUT_DIR) with open(os.path.join("./out/", "train.txt"), 'w') as f_train: for session_id, plan_list in plan_map.items(): if session_id not in train_data_dict: continue cur_map = train_data_dict[session_id] if cur_map["pid"] != "": cur_map["profile"] = profile_map[cur_map["pid"]] else: cur_map["profile"] = [0] del cur_map["pid"] whole_rank = 0 for plan in plan_list: whole_rank += 1 cur_map["whole_rank"] = whole_rank flag_click = False rank = 1 for plan in plan_list: if ("transport_mode" in plan) and (session_id in session_click_map) and ( int(plan["transport_mode"]) == int(session_click_map[session_id])): cur_map["plan"] = plan cur_map["label"] = 1 flag_click = True # print("label is 1") else: cur_map["plan"] = plan cur_map["label"] = 0 cur_map["rank"] = rank rank += 1 cur_json_instance = json.dumps(cur_map) f_train.write(cur_json_instance + '\n') if not flag_click: cur_map["plan"]["distance"] = -1 cur_map["plan"]["price"] = -1 cur_map["plan"]["eta"] = -1 cur_map["plan"]["transport_mode"] = 0 cur_map["rank"] = 0 cur_map["label"] = 1 cur_json_instance = json.dumps(cur_map) f_train.write(cur_json_instance + '\n') else: cur_map["plan"]["distance"] = -1 cur_map["plan"]["price"] = -1 cur_map["plan"]["eta"] = -1 cur_map["plan"]["transport_mode"] = 0 cur_map["rank"] = 0 cur_map["label"] = 0 cur_json_instance = json.dumps(cur_map) f_train.write(cur_json_instance + '\n') build_norm_feature() if __name__ == "__main__": preprocess()